A deep learning platform for real-time global longitudinal strain measurement showed excellent correlation (r=0.94) with manual reference and reduced median acquisition time by 57%.
Observational (n=50)
Does a deep learning platform improve the time efficiency and precision of global longitudinal strain measurements compared to conventional workflow in patients undergoing echocardiography?
A deep learning platform for real-time global longitudinal strain measurement is feasible, precise, and significantly reduces analysis time compared to conventional manual workflows.
Effect estimate: bias -1.3, r = 0.94
Absolute Event Rate: 14.8% vs 16.2%
BACKGROUND: Left ventricular (LV) global longitudinal strain (GLS) offers advantages over LV ejection fraction, including improved diagnostic sensitivity, reproducibility, and prognostic value. However, current semiautomatic analyses are time-consuming and operator dependent, impeding widespread adoption of GLS in routine clinical practice. OBJECTIVES: We aimed to assess the feasibility, precision, and time efficiency of GLS measurements using a deep learning (DL) platform that performs real-time GLS analysis during image acquisition and incorporates DL tools to support standardization, to evaluate whether DL-assisted acquisitions can enhance image quality metrics relevant to strain analyses. METHODS: A DL platform was developed for fully automated real-time GLS analysis, including tools that detect and alert the operator to foreshortening or baseline drift. In this controlled prospective study, 50 patients (mean age, 56 years; 64% male) were included. Two image sets were acquired by different operators using the DL platform and a conventional workflow, and GLS and image quality were compared. RESULTS: Overall feasibility of DL-based GLS measurements was 94%. Absolute GLS was 14.8 ± 3.2 using the DL platform workflow and 16.2 ± 3.3 with manual reference measurements, with a bias of -1.3 and limits of agreement ranging from -3.5 to 0.8. Correlation was excellent (r = 0.94). Images acquired with the DL platform showed significantly less baseline drift and borderline improved territorial strain agreement than the reference acquisition. The median time obtaining GLS with the DL platform was reduced by 57% compared to the conventional workflow, from 4 minutes and 48 seconds to 2 minutes and 4 seconds. CONCLUSION: The DL platform for fully automated real-time GLS measurements was feasible, precise, and time efficient. Real-time DL-based feedback allows operators to optimize images during acquisition, thus improving quality metrics relevant to GLS analyses. Implementing this method in clinical practice could streamline workflow and improve efficiency in the echocardiographic laboratory.
Holmstrom et al. (Tue,) conducted a observational in Left ventricular global longitudinal strain assessment (n=50). Deep learning platform for real-time GLS analysis vs. Conventional workflow (manual reference measurements) was evaluated on Absolute global longitudinal strain (GLS) and measurement time (bias -1.3, r = 0.94). A deep learning platform for real-time global longitudinal strain measurement showed excellent correlation (r=0.94) with manual reference and reduced median acquisition time by 57%.
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